GTM Lead at Expa Health

Job Title

GTM Lead

Not Disclosed

Expa Health is a seed-stage, AI-native startup based in San Francisco building the first generative AI recruiting platform specifically for hospitals and healthcare organizations to find licensed clinical talent.

You will own the full content-led demand generation engine, driving a bottoms-up GTM motion at Expa Health. By creating viral Linked In content, hosting webinars, and building a newsletter, you will convert healthcare recruiters into self-serve users. This hands-on role is critical in scaling the platform from early traction to over $5M in revenue.

Remote

• Shape the future of healthcare staffing by building the first generative AI recruiting platform for clinical roles from the ground up as a founding team member.
• Gain founding-team equity and direct influence on the product roadmap while working in a high-autonomy, remote-first culture with an elite team of under 10 people.
• Lead a transition from enterprise-led sales to a high-scale PLG model, directly impacting patient care capacity by helping hospitals fill critical nursing and therapy roles.

• Build and execute a bottoms-up GTM engine using original, viral Linked In content to attract and engage healthcare recruiters who struggle to find clinical talent.
• Launch and scale a dedicated newsletter and webinar series designed to educate the market on AI-driven clinical hiring and licensure constraints.
• Directly manage the end-to-end conversion funnel from social engagement to self-serve platform adoption, driving the company's 0-to-1 revenue growth strategy.

• Proven track record of 0-to-1 revenue growth, with specific experience taking a product from early traction to at least $5M in annual recurring revenue.
• Expert-level Linked In content creator who has built a personal audience and has experience executing content-led demand generation for self-serve or PLG models.
• High-output builder DNA who thrives in an autonomous individual contributor role and is comfortable navigating the ambiguity of an early seed-stage environment.